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Fast Facts

Our program was among the first to partner with the American Medical Informatics Association (AMIA) to provide their 10x10 certification.

MSBI Program graduates were among the first group of physicians in the nation to become board certified in clinical informatics.

MSBI Program students have completed internship and practicum experiences at many sites including Cleveland Clinic Florida, Palmetto General Hospital, National Institutes of Health, and Mayo Clinic.

The Biomedical Informatics Program is growing with faculty positions currently available.

Our students have won the South Florida Chapter Health Information Management Systems Society (HIMSS) Scholarship three times.

As a part of the Biomedical Informatics Program’s Academic Organizational Affiliate status with the Health Information Management Systems Society (HIMSS), all full time students are entitled to a free HIMSS membership!

The Biomedical Informatics Program was ranked No. 6 out of 25 on BestMedicalDegrees.com’s 2015 list of Best Value Online Master’s in Health Informatics and Health Information Management.

NSU's Biomedical Informatics Program was was the first graduate program in health informatics in the state of Florida.

Students & Alum

"NSU's M.S.B.I. program gave me the tools and knowledge to navigate the health informatics industry with confidence. It prepared me to provide meaningful contributions to seasoned professionals and organizations. Two years after graduation my relationship with the program is still going strong."

"Tons of work to reach this simple goal: 'The right information, to the right person, at the right time.' The healthcare industry is not there yet, but with programs such as NSU’s Biomedical Informatics program, it will be soon."

"The program helped me turn an adversary into an opportunity. Tenacity and a strong drive led to my success in the M.S.B.I. program and in my world."

Stephen Amoah, M.S.B.I. (’14), Ph.D. Student

"The faculty and staff of the NSU MS in Biomedical Informatics program are amazing! I was nervous to enroll in this program because I had little knowledge of the healthcare industry. With preparation from the pre-requisite courses, I was able to obtain an entry level position in healthcare. Approaching the end of the program, I was able to obtain a promising healthcare IT position, where I could effectively contribute my knowledge and skills at a large healthcare organization."

Kenneth Simpson, Current Student, Application System Analyst II at University of Maryland Medical System

"Certainly, I had such an amazing experience[with my practicum],where I could put what I've learned into practice and also I've explored other learning possibilities and gained some new skills. "

Eswald Fertil, Current Student

Congratulations to alum Tracy Eckerle for passing the ANCC Certification for Informatics Nursing!

M.S. in Biomedical Informatics Curriculum

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Curriculum

The NSU COM Biomedical Informatics Program is designed to prepare students to meet the challenges and opportunities of a career in the health information technology sector. The three major focus areas of the NSU COM Biomedical Informatics Program's curriculum are: computer science with a medical informatics focus, clinical informatics with a concentration in the areas of applications and evaluation, and business and management of health information technologies.

The NSU COM Biomedical Informatics Programs can be completed entirely online allowing working professionals to obtain their degree or certificate without career disruption. The skills-based curriculum includes courses leading to Lean Six Sigma Green Belt, CPHIMSS, and NextGen certifications. A paid internship at NSU's clinics is also available, in addition to a number of practicum experience opportunities in the surrounding community and beyond.

Curriculum Requirements

The innovative skills-based curriculum leading to a Master of Science in Biomedical Informatics degree consists of the following didactic courses offered predominantly in an online fashion via NSU's state-of-the-art web-based, distance-learning technology. Students are required to complete a practicum project consisting of hands-on practical work within a health information technology or other appropriate environment.

All students admitted into the Master of Science degree program in Biomedical Informatics require completion of this 1.0-credit hour orientation. Students are required to complete MI 5000 concurrently with their first sequence of courses in the program of study, and will be automatically enrolled in the orientation course (online or in-person) during their first term of study.

Prerequisite(s): None.

Learning Objectives:

Complete computer skills assessment.

Understand and identify services provided by the Office of Student and Alumni Affairs.

This on-line, interactive course is an introductory survey of the discipline of biomedical informatics. This course will introduce the student to the use of computers for processing, organizing, retrieving and utilizing biomedical information at the molecular, biological system, clinical and healthcare organization levels through substantial, but not overwhelming, reading assignments. The course is targeted at individuals with varied backgrounds including medical, nursing, pharmacy, administration, and computer science. The course will describe essential concepts in biomedical informatics that are derived from medicine, computer science and the social sciences.

Learning Objectives:

Demonstrate in writing and verbally a basic understanding of the learned concepts of biomedical informatics and their direct application to healthcare.

Demonstrate the ability to compare, select, apply and integrate multiple technologies in and across a healthcare organization via leadership, clinical, administrative, or other staff positions.

Discuss key legal and ethical issues that must be considered when implementing biomedical technology and supporting information systems to include initiatives such as the Electronic Health Record.

This course covers basic to intermediate knowledge of the concept, the design, and the implementation of database applications in healthcare. Students will study tools and data models for designing databases such as ER Model and SQL. The course also covers Relational DBMS systems such as SQL Server, Access, Oracle and MySQL. In addition, database connectivity design (essential in data-driven web development) and database administration will also be introduced. Students will practice designing, developing and implementing a test relational online health IT database application through a comprehensive project that contains the above topics.

Learning Objectives:

Identify the key elements of database management system and applications in healthcare.

The understanding of telecommunications and networking is imperative for adequate functioning of healthcare organizations. This is due to the convergence of computing, data management, telecommunications, and the growing applications of information technology in the healthcare arena and medical facilities. The knowledge of these key areas of information systems also becomes essential for competitive advantage. This course combines the basic technical concepts of data communications, telecommunications and networking with the healthcare IT management aspects and practical applications.

Learning Objectives:

At the end of the course, the students should be able to:

Identify current concepts of data communications and networking and how to implement them in a medical treatment facility

Execute a network implementation by having managerial knowledge of the technical aspects of data communications and computer networks

Identify various security risks to a network and ways to minimize them

Assess the current trends in telecommunications and networking and the implications for health care and medical facilities

The need to create effective, new solutions and innovative interventions to deliver quality patient care outside of the traditional medical setting is at the forefront of society today. The basis of this course will be providing a solid educational foundation for systems design & analysis, as it relates to current and future healthcare systems. In addition, this course will build upon the fundamental systems design & analysis principles to explore current and future healthcare systems that will include integration of disparate clinical healthcare systems, mobile technologies, as well as a combination of remote-monitoring technology, sensors, and online communications and intelligence to improve patient adherence, engagement and clinical outcomes.

Learning Objectives:

In the role of a systems analyst, investigate and demonstrate the foundations of systems analysis & design theory and applications as it relates to healthcare systems

This on-line course is an introduction to the management of employees in healthcare organizations (HCO’s). Students will gain a working knowledge of how to manage personal, interpersonal, and group processes by having the interpersonal skills to assume responsibility for leading and promoting teamwork among diverse stakeholders. Students will learn to manage individual and group behaviors in improving organizational productivity and performance. Students will be able to apply newly learned organizational skills, developed through experiential and application based learning scenarios in the form of case studies as well as from their home, work, and educational observations and experiences. It is anticipated that this practical learning experience can be transferred to their day to day managerial responsibilities.

Learning Objectives:

Evaluate basic concepts of organizational behavior and organizational development

Lean Six Sigma for Health Care (Yellow Belt) participants will learn the basic philosophy, tools, and techniques to deliver breakthrough business improvements that will reduce waiting times, improve quality, and reduce costs in a health care environment. More specifically, they will learn to apply a comprehensive set of 15-20 Lean Six Sigma process improvement tools by using the PDCA (Plan, Do, Check, Act) problem solving model. They will learn techniques for both quantitative and qualitative analysis, as well as methods and tools for waste reduction and process enhancement and acceleration. The course also covers how to map out processes and identify sources of variation, as well as to gain a basic understanding of descriptive statistical analysis. Finally, they will learn how to perform basic pilot studies and analyze the results, in order to determine the most effective way to improve and stabilize processes. Candidates work on either an integrated health care case study or on an actual business project, and will apply classroom techniques to the project.

Learning Objectives:

Summarize Lean Six Sigma history and philosophy and describe how it applies to modern health care organizations.

Identify opportunities for system and process improvement in health care settings.

Use basic problem solving and critical thinking skills and apply systems thinking to quality improvement projects in hospitals and other clinical settings.

This course will provide an introductory, hands-on experience for life science researchers in bioinformatics using R and Bioconductor. Emphasis will be placed on accessing, formatting, and visualizing genomics data. Most analyses will deal with “little” data (no mapping or assembly of short reads), but some techniques to work with “big” data (e.g. BAM files) will be covered. Lecture and lab will both be held in a computer lab, so lecture will be “hands-on”. Working in small groups is encouraged.

Learning Objectives:

Students will learn the fundamentals of bioinformatics analyses of genomics data using R and Bioconductor.

Students will gain a greater appreciation for bioinformatics and the parallels with “wet bench” experiments.

Students will be introduced to the concept of “literate programming” and how it can be applied to document their work are write legible reports.

Students will be prepared for more advanced courses in R or bioinformatics, or for continued self-learning.

Pre-Requisite: MI 5200, and HIPAA modules are prerequisites for MI7000. In addition, CITI certification is required for research projects. Students should complete HPD Medicine Module #13. The course director may also require specific electives to be completed depending on the nature of the project that the student chooses to perform.

Please note that students must have a GPA of at least 3.00 to be eligible to register for or participate in practicum work.

Description:

This is a required course for all MSBI students. The practicum allows the student to select an area of interest in which to apply the theories, concepts, knowledge, and skills gained during the didactic courses in a real-world setting. The student will work under the supervision of a site-based preceptor and an NSU-based faculty advisor.

The student is expected to acquire skills and experiences in the application of basic biomedical informatics concepts and specialty knowledge to the solution of health information technology (HIT) problems. Students will be actively involved in the development, implementation, or evaluation of an informatics-based application or project.

A specific set of measurable learning objectives and deliverables will be determined by the student, the site preceptor, and the NSU-based faculty advisor. These learning objectives must be approved by the course director. The student’s area of interest would be determined at an earlier point in the program or by the needs of the precepting organization.

The practicum is evaluated by completion of an ePortfolio. The ePortfolio is an evidence based digital format method to assess the quality and quantity of learning gained from a student practicum experience. The ePortfolio is standardized in its structure and format yet individualized in its content for each student. Overall, the ePortfolio is a goal-driven documentation of professional growth and achieved competencies during the practicum. The ePortfolio combines self-reflection, instructor assessments, and documentation supplied by students (evidence/samples) to document what they learned/produced, and is used to help students prepare for career transition/development.

Students are responsible for finding their own practicum site. Once a site is located, the Program Office will facilitate a legal affiliation agreement between the site and the Program. Some practicum sites may require background checks, drug screening, and immunization records. Students are responsible for any associated costs.

Learning Objectives:

Individualized

ADDITIONAL SUGGESTED COURSES (IF NEEDED):

This self-paced online course provides a basic introduction to medical terminology, using the body systems approach. It provides the student with guided practice and assessment of prefixes, suffixes, word roots, and combining forms. It includes vocabulary, definitions, spelling, and pronunciation. A problem-solving approach to learning is the key strategy and focus of this course.

Learning Objectives:

Demonstrate use of a medical dictionary.

Define prefixes and suffixes and properly use them with word roots and combining forms to build medical terms.

This course provides students with an overview of healthcare management covering fundamental concepts and theories including information systems management, operational leadership, strategic leadership, governance, foundations of clinical performance, clinical support services, community health, knowledge management, human resource management, the environment of care management, financial management, and marketing. A common theme of high performance healthcare organizations (HCO’s) are that they embraced a culture of transformational management and evidence-based management, both are carefully weaved throughout course. Also emphasized are critical management activities including measures and metrics, benchmarking, negotiated goal setting, and continuous improvement, which are all essential to high performance HCO’s.

Learning Objectives:

Describe the role and functions of management in healthcare facilities including the application differences in various kinds of HCO’s and programs

Assess special problems associated with the implementation of modern management programs in healthcare settings and possible methods of dealing with these special requirements

Identify various management decision-making tools used by healthcare administration in the management process with attention to methods of measuring effectiveness and efficiency

Correlate and demonstrate the ability to share practical opinions and viewpoints with various health professionals regarding healthcare administration methodologies

Explain the role of integrative healthcare systems and other applications in the strategic competitive environment of the healthcare system

Illustrate the ability to use organizational structure as a management tool and assess alternative structures and their implications on performance

Compare and contrast the legal structure of organized healthcare delivery system models

Formulate a strategic competitive plan based on stakeholder analysis and structured internal assessment of a selected healthcare organization

Describe the relationship of managed healthcare systems and the associated provider reimbursement and quality management

Analyze financial management, planning methods, the resulting role in reporting, control and influence on decision-making in HCO’s

This course is designed to introduce students to architectures of information systems and the logic used by computers to solve problems. Even though many students consider themselves “tech savvy” due to their prior use of information systems, most students do not have an appreciation of how computers actually work. In their future roles as biomedical informaticists, they will need to have a deeper understanding of how computers actually operate. The course will provide this deeper understanding of computer systems.

Learning Objectives:

Describe the standard von Neumann architecture of modern computers

Discuss how data is represented in the computer

Explain the fundamental nature of algorithms

Demonstrate the logical thinking required to develop algorithms

Produce pseudocode to “implement” various algorithms to solve specified problems

The basic content of the course will be drawn from the IEEE Computer Society’s Guide to the Software Engineering Book of Knowledge (SWEBOK) with the addition of specific exposure to programming in the object oriented and Internet environments. It will focus on developing the knowledge and skills necessary for a biomedical informaticist to participate in the development of informatics systems, including the ability to understand and interact effectively with software development teams in healthcare environments. It will also give the student experience in actually developing software systems in JAVA, XML, and JSON for healthcare applications. The student will become knowledgeable about software development life cycles, such as waterfall and Agile (e.g., Scrum) methodologies, that are commonly used in healthcare information technology (HIT). Finally, the students will become familiar with the economic issues related to software development/maintenance in healthcare.

Learning Objectives:

Examine the basic knowledge of software engineering as contained in the SWEBOK

Enumerate the foundations of both software and engineering

Discover the basic elements of software requirements, design, construction, testing, maintenance, and configuration management as practiced in the healthcare setting.

ELECTIVE COURSES - A TOTAL OF 12 CREDITS (4 COURSES) MUST BE TAKEN:

This course covers major concepts, systems and methodology in managing healthcare information systems. Topics will include concepts in: system implementation and support, information architecture, IT governance in health care, information systems standards, organizing IT services, strategic planning, IT alignment with the healthcare facility, and management’s role in major IT initiatives.

Learning Objectives:

Upon completion of the course the student will be able to:

Design strategies for management in acquiring, planning, and implementing major healthcare IT initiatives;

Implement sound project management methodologies in healthcare IT systems, which can be critical to the strategic plan of the facility;

Evaluate technologies such as electronic medical records (EMRs), enterprise resource planning, or enterprise collaboration systems, which can facilitate a healthcare facility’s business processes;

Integrate the roles of stakeholders, IT staff, and management in designing and implementing health information technology (HIT) projects;

The dynamics of human-computer interaction (HCI) directly impacts health care. This course will introduce the student to usable interfaces and the study of social consequences associated with the changing environment due to technology innovation.

This course introduces students to theoretical, statistical, and practical concepts underlying modern medical decision making. Students will be provided a review of the multiple methods of knowledge generation for clinical decision support systems (CDSS) and create their own prototype of CDSS. Current implementations of stand-alone and integrated CDSS will be evaluated. Techniques for planning, management, and evaluation of CDSS implementations will be reviewed. Human factors, including work-flow integration, and the ethical, legal and regulatory aspects of CDSS use will be explored, as applicable to commercial implementations in patient care settings. Future models of healthcare, supported by CDSS and evidence-based medicine, will be discussed and reviewed.

Learning Objectives:

Describe the scope and kinds of clinical decision support systems; analyze CDSS effectiveness in terms of implementing for diagnostic and therapeutic purposes.

Evaluate the linkage of CDSS to the basic concepts of evidence-based medicine.

Analyze technology and business characteristics of successful CDSS implementations using recent industry cases as guidelines and input to build student’s own attributes of an effective CDSS implementation.

Recognize business and clinical implementation and maintenance challenges in commercial CDSS projects, as well as possible resolutions to these challenges.

This interactive course will introduce students to various evaluation methods for healthcare informatics systems, projects and proposals. Students will consider both quantitative and qualitative methods of evaluation as they examine the design and implementation processes.

This course focuses on the principles and reasoning underlying modern biostatistics and on inferential techniques commonly used in public health research. Students will be able to apply basic inferential methods in research endeavors and improve their abilities to understand the data analysis of health-related research articles.

Learning Objectives:

For a given study the students will be able to formulate the research question(s) and the corresponding statistical hypotheses.

Describe the roles biostatistics serves in the discipline of public health.

Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.

Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.

Apply descriptive techniques commonly used to summarize public health data.

Apply common statistical methods for inference.

Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.

Apply basic informatics techniques with vital statistics and public health records in the description of public health characteristics and in public health research and evaluation.

Apply sample size and power calculation techniques.

Interpret results of statistical analyses found in public health studies.

Develop written and oral presentations based on statistical analyses for both public health professionals and educated lay audiences.

Learn the rules of research with human subjects by taking the Citi course.

Using computing statistical packages such as JMP, SAS and EpiInfo, the students will be able to apply the biostatistical methods producing results and interpreting the computer output in an appropriate.

Examines basic principles and methods of modern epidemiology used to assess disease causation and distribution. Students develop conceptual and analytical skills to measure association and risk, conduct epidemiological surveillance, evaluate screening and diagnostic test, as well as investigate disease outbreaks and epidemics.

Learning Objectives

Identify and define the core concepts and terminology of Epidemiology

Define and calculate measures of disease frequency and mortality including incidence, prevalence and mortality rates (crude and adjusted)

Contrast the concepts of association and causality and explain the “criteria for causality.”

Understand and apply the basic measures of the exposure-disease association: absolute, relative and attributable risk

Identify the descriptive study designs and discuss their applications and limitations

Compare and contrast the designs of case-control and cohort studies, including relative strengths and weaknesses

Describe the design of interventional studies including clinical trials.

Become familiar with data sources used in epidemiology and describe the utility and components of disease surveillance systems

MI-6404 is an elective course designed as a student/self-directed course. In consultation with the chosen advisor/mentor and the course director, the student will determine a focused topic of quasi-independent study, research, or other appropriate learning activity. A final paper or other appropriate document(s) will serve as documentation of having met the mutually agreed upon objectives.

Public health informatics is the systematic application of information and computer science and technology to public health practice, research and learning. This course focuses on developing the knowledge and skills of systemic application of information, computer science, and technology to public health practice. Students will acquire a basic understanding of informatics in public health practice, and be able to apply the skills of using some informatics tools in public health practices.

Learning Objectives:

Develop a true understanding your personal strengths and talents.

Articulate how you have used your strengths in your daily work and personal performance.

Analyze which strengths you can apply best to various tasks required in the use of health technology.

Assess the strengths of your class and determine which person performs best in different situations.

Evaluate which of your strengths are best suited for various positions in the Health IT industry including healthcare organizations.

Recognize the importance of results driven organizations using individual talents to increase effectiveness.

Conclude which direction HIT should move in to best manage patient care.

Conclude which HIT applications are best suited for evaluating a patient care program.

Conclude which talents are best suited for being in a leadership position in HIT.

Conclude which talents are best suited for developing a strong HIT communication program.

This course provides an introduction to the skills of grant writing in biomedical informatics. Each student will submit a completed grant application as a culminating experience. This course introduces students to grant development and preparation so that they can participate in the process of obtaining public or private funds to support research, education and/or service projects.

Learning Objectives:

Describe the elements of successful and unsuccessful grant applications

This course is an in-depth review of basic planning & evaluation techniques for the implementation of community health care program. The course is designed & will be taught employing comparative methodology. The material will be taught using examples & experiences from multiple international examples. The course covers the interdependence between policy and planning and management. It will consist of policy analysis techniques as well as the conceptual framework for the planning and management of health care programs. The course also reviews essential methods for effective planning & evaluation considering the economic, political epidemiological, demographic, and other components that contribute to the assessment of health needs and resource allocation.

Consumer Health Informatics is a relatively new application of information technologies in the field of health care that aims to engage and empower consumers to become involved in their health care. This course provides an introduction to, and overview of, consumer health informatics, mobile health (mHealth), and social media applications used in healthcare. It explores the development of consumers as ePatients and tools such as personal health records (PHRs), as well as the fluid nature of social media in medicine and the emerging area of mobile health (mHealth). Students will learn from a combination of lectures and a hands-on approach of interacting directly with the tools and technologies discussed.

This course immerses students in the technical, business, cultural and organizational dynamics typically encountered during HIT systems selection and contract negotiation process. Real world case studies, replete with dynamic political, financial and technical roadblocks and opportunities, will be used to introduce the student to skills required to make the best cultural decisions and negotiate a viable contract.

Learning Objectives:

Discuss and document the six phases of the procurement.

Analyze factors that are important when qualifying and selecting suppliers for a project requirement.

Examine the key factors, including risk factors that affect buyer/supplier decisions concerning contract pricing and the selection of the proper contract type.

Analyze the application of e-Procurement and other types of supplier bidding models available.

Evaluate technical, management, commercial and ethical requirements, and then prepare a Request for Proposal (RFP).

Determine the key factors used when negotiating an agreement or evaluating competitive proposals and establish a negotiating strategy.

Analyze factors that are important when qualifying and selecting suppliers for a project requirement and;

Develop the skills to negotiate fair and ethical contracts which beneficially serve the business needs and missions of all parties involved.

This course provides the conceptual and technical skills needed in leading health information technology. It is designed to create a profound understanding of leadership at the cognitive and action levels to enable health information leaders to optimize decision-making in the workplace. Students review remarkable leaders, organizations, and teams in order to hone their own observation, sense-making, and innovating skills in a health information setting. This leadership course reviews and builds upon the basic knowledge of leadership provided in the organizational behavior course by expanding the scope and depth of the student's knowledge of leadership theories, conflict management techniques, and by developing the student's self-knowledge of his or her preferred leadership styles.

Learning Objectives:

Describe the historical development of leadership theory and its impact upon health information technology,

This class will provide students with introductory understanding of clinical analysts’ daily responsibilities and functions within hospitals. Students will be introduced to daily operations of clinical software systems and lead to understand how such systems are used by health care organizations to provide quality care services.

Learning Objectives:

Analyze the management and support of clinical users’ HIT business needs.

Evaluate how clinical information systems are used to improve quality of care.

Telemedicine is the exchange of health information from one side to another utilizing electronic communications. This course introduces the student to fundamental concepts and knowledge of telemedicine technologies, its application and usage including: essential aspects of communication networks and services; wired and wireless infrastructures; safeguarding medical data including health information privacy; systems deployment; patient monitoring and care; information processing; and future trends in telemedicine will be studied. Discussions areas include telemedicine: technical perspectives; scalability to support future growth; integration with legacy infrastructures and interoperability; history; trauma; emergencies and disasters; clinical applications; and other critical components of telemedicine technologies.

Learning Objectives:

Define the capabilities, challenges and limitations of current information technologies utilized in healthcare information communication systems in telemedicine;

Describe the technical components used in medical information processing;

Lean Six Sigma for Health Care (Green Belt) participants will learn intermediate level tools, and techniques to deliver breakthrough business improvements that will reduce waiting times, improve quality, and reduce costs in a health care environment. More specifically, they will learn to apply a comprehensive set of 15-20 Lean Six Sigma process improvement tools by using the DMAIC (define, Measure, Analyze, Improve, and Control) problem solving model. They will learn techniques for both quantitative and qualitative analysis, as well as methods and tools for work flow enhancement and acceleration. The course also covers how to map out processes and identify sources of variation, as well as to gain a basic understanding of inferential statistical analysis. Finally, they will learn how to perform how to implement lean management tools and philosophy, in order to improve and stabilize processes. Candidates work on either an integrated health care case study or on an actual business project, and will apply class techniques to the project. There will be additional practice with basic tools to help promote mastery.

Identify opportunities for system and process improvement in health care settings by using project selection and solution selection matrices.

Use intermediate level problem solving and critical thinking skills on quality improvement projects in hospitals and other clinical settings.

Identify valid and critical to quality customer and business requirements and related measures and then turn the data into actionable information to manage and improve organizational processes.

Use break-through equations and cause and effect analysis to identify the important X and Y measures in processes and systems.

Apply the DMAIC model in accordance with Lean Six Sigma principles.

Map out health care value streams and other high level processes to identify sources of variation, and to acquire a beginning-level understanding of inferential statistical analysis, as well as learn to perform basic experiments and analyze data to determine the most effective way to improve and stabilize processes.

Conduct measurement system analysis to determine measurement reliability and validity.

This course will provide students with the opportunity to learn the fundamentals of set-up and using the applications of one of the most commonly used electronic health record systems in the US, NextGen, in clinical settings. Students will be required to complete the NextGen e-learning modules before the on campus hands on training sessions.

This course is required for the competitive internship opportunity in the NSU clinics (more details to follow).

Learning Objectives:

Demonstrate the ability to use and set-up NextGen EHR and ExpressRx applications.

Use the Knowledge Base Model (KBM) templates and workflows

Complete at least one demonstration of Stage 1 Meaningful use with NexGen solutions.

Evaluate the current use of clinical application of NextGen at NSU clinics

Identify ways to improve the functionality and workflow for NSU clinics

This course will provide students with a preliminary understanding of the theory and practice of medical image processing and analysis in healthcare. Basic concepts and fundamentals of medical image processing and analysis will be described in the course. The application of medical image processing and analysis in biomedical information systems will be discussed. Students will be introduced to the fundamentals and methodology of medical image processing, image analysis, image compression, and molecular imaging.

Learning Objectives:

Describe the principles and modalities of medical imaging analysis and processing.

Identify software and hardware needs for the implementation and design of medical imaging analysis and processing applications.

Differentiate various procedures used in the computer representation of images such as image enhancement, image restoration, image reconstruction, and other image analysis techniques.

Explain current medical imaging analysis and processing techniques.

Discuss the relevance of medical imaging analysis applications in healthcare.

This course will introduce students to geographic information systems (GIS) to map and spatially analyze public health and demographic data. Students will learn the fundamentals of the ArcMap software system and ways to integrate cartography into biomedical informatics practice. Beyond use of GIS for cartography, this course will also examine ethical issues and methods of analyzing demographic and spatial health patterns using GIS and demography analysis methods. The versatility of GIS in a public health setting will be examined and will include exercises involving GIS applications in health marketing, demography, epidemiology, and health care systems. For example, we will look at how different socioeconomic groups use urban spaces differently in terms of transportation and how these differences in navigation impact contact points for health marketing. Other issues covered in the class will be the ethics of GIS, manipulation of data, sources of data, and understanding some commonly used public health datasets such as the YRBS, BRFSS, and US Census.

The course will introduce the clinical workflow analysis as a method of choice to improve clinical processes in healthcare delivery systems. Students will review the primary objectives for process improvement in clinical healthcare: outcome quality (including patient safety) and the development of health information technology (HIT) to support the Electronic Health Record (EHR) with initiatives showing a significant impact on clinical workflows (e.g. meaningful use). Students will define the functional components of the healthcare activities and learn to map on a flowchart the standard symbols used to represent all tasks and steps, decision points, resources, and outcomes of the clinical workflow. Students will apply the tools of workflow analysis by assessing a workflow in a healthcare setting using graphical representations of 0the workflow phases (current state, desired state), and process defects identification and classification. The course will introduce the quantitative measures of workflow improvement used in Lean Six-Sigma. Students will formalize a proposal for an intervention aimed at the modification and optimization of a clinical workflow.

Learning Objectives:

Define business processes and process improvement in healthcare from the patient, clinician, and analyst perspective.

Identify two major challenging objectives for improvement in healthcare: 1) quality and patient safety and 2) meaningful use of EHR.

Map the components of a clinical workflow chronologically into a flowchart, using the standard symbolic shapes for tasks and steps, decisions and processes, resources, inputs and outputs, connectors, and outcomes.

In the ever changing world of information and global economic competition it is crucial that individuals and organizations understand their personal as well as group talents. Today’s educational, healthcare and institutional structures lack leadership and cutting edge thinking. By applying strength based leadership practices one comes to understand their own as well as the group’s strengths and talents and is able to apply these practices in their daily work as well as in leadership roles.

The course will produce a personal understanding of individual as well as group personality/strengths and how these evolve and affect performance in individuals. Students will develop a better self-awareness of what strengths they possess and how this affects personal as well as work performance. It demonstrates how leaders if it is a chosen career path can continue to grow and how to develop each of your group’s talents to maximize the performance of your team and organization. The Affordable Care Act will be incorporated and students will discover what individual as well as organizational talents must be utilized to improve patient care in the future utilizing technology.

Learning Objectives:

Develop a true understanding your personal strengths and talents.

Articulate how you have used your strengths in your daily work and personal performance.

Analyze which strengths you can apply best to various tasks required in the use of health technology.

Assess the strengths of your class and determine which person performs best in different situations.

Evaluate which of your strengths are best suited for various positions in the Health IT industry including healthcare organizations.

Recognize the importance of results driven organizations using individual talents to increase effectiveness.

Conclude which direction HIT should move in to best manage patient care.

Conclude which HIT applications are best suited for evaluating a patient care program.

Conclude which talents are best suited for being in a leadership position in HIT.

Conclude which talents are best suited for developing a strong HIT communication program.

The course will expose students to healthcare “big data” focused on current needs such as population health, outcome reporting, clinical decision support, physician quality measurement, and various other measures including CMS initiatives such as meaningful use and Medicare and payer quality reporting requirements. The course will use current real world problem scenario’s where data analytics and visualization can be applied to successfully report on and solve various problem prevalent in today’s value based payer model. Students will learn how to do large scale data mining and the infrastructures needed to support the various system designs such as Hadoop ecosystems and Hadoop based tools. The student will be exposed to the application of predictive analytics specific to healthcare with an understanding of using data to help deliver quality and safe patient care as well as data driven methods of improving care. The course will expose students to real time data analytics where data is collected and reported on around the clock. The course will also expose student to mobile data acquisition and analysis coming from various local and remote devices. This course will introduce students to data visualization methods which will teach them how to communicate analytical insights to both technical and non-technical audiences.

Learning Objectives:

Teach the students current techniques used to mine and report healthcare analytics data using both structured and unstructured data models.

Produce data reports using both predictive and prescriptive output modeling.

Create reports using spreadsheets, databases, and PowerPoint tools that make meaningful use of complex healthcare data

Communicate and display healthcare data for understanding of technical and non-technical audiences.

Gain knowledge on the various forms of hardware infrastructure needed to manage and distribute information within various healthcare system models from smaller scale department specific data warehouse models to big data infrastructures with fault tolerance and fail over disaster recovery architecture.

This course is a continuation of MI 6424 (Introduction to Healthcare Analytics and Data Visualization I). The course will expose students to healthcare “big data” focused on current needs such as population health, outcome reporting, clinical decision support, physician quality measurement, and various other measures including CMS initiatives such as meaningful use and Medicare and payer quality reporting requirements. The course will use current real world problem scenarios where data analytics and visualization can be applied to successfully report on and solve various problem prevalent in today’s value based payer model. Students will learn how to do large-scale data mining and the infrastructures needed to support the various system designs such as Hadoop ecosystems and Hadoop based tools. The student will be exposed to the application of predictive analytics specific to healthcare with an understanding of using data to help deliver quality and safe patient care as well as data driven methods of improving care. The course will expose students to real time data analytics where data is collected and reported on around the clock. The course will also expose student to mobile data acquisition and analysis coming from various local and remote devices. This course will introduce students to data visualization methods, which will teach them how to communicate analytical insights to both technical and non-technical audiences.

Learning Objectives:

Describe current techniques used to mine and report healthcare analytics data using both structured and unstructured data models.

This advanced cognitive engineering systems course expands upon introductory topics presented as parts of the clinical decision support and analytics courses to take a deeper dive into data science and artificial intelligence algorithms, with application to such medical specialties as oncology, cardiology, radiology, and neurology. It provides students with skills necessary to undertake programmatic analysis of patient information data sets, apply unsupervised learning techniques to enhance outcomes of the predictive and prescriptive analytics methods, use supervised learning methods to represent evidence based guidelines and detect medical fraud, compare and analyze graphs and images, and apply natural language processing techniques to ingest and analyze text information.

Learning Objectives:

Determine the need for and basic concepts of artificial intelligence in healthcare.

Apply AI knowledge and skills in various disease management situations to enhance practical application of the subject and adopt innovative approaches to linking disparate pieces of information into cohesive medical data science strategy.

Integrate understanding of the clinical decision support, database, basic programming, and decision tree building knowledge into ability to solve complex problems through systematic approaches and step-by-step implementations of building applied solutions to medical problems.

This course would introduce students to a variety of mathematical techniques that are commonly used in healthcare analytics and biomedical informatics. The emphasis would be on developing an understanding of the methods, their uses, and their limitations. Mathematical rigor would not be emphasized, but an understanding of the meaning and uses of the techniques. The instruction would also include inculcating a mathematical mindset in the students which would allow them to extend their knowledge and understanding to further areas as needed in their future endeavors.

Learning Objectives:

Examine mathematical concepts and techniques used commonly in healthcare analytics and biomedical informatics

Discover the meaning of the techniques covered

Demonstrate the uses and limitations of the techniques covered

Solve assigned quantitative problems in these areas

Develop the mindset to extend knowledge and skills in quantitative methods beyond what is presented in the course

This course provides a comprehensive and rigorous introduction to big data analytics in healthcare. It will describe the hardware/software infrastructures that are used today for big data (e.g., Hadoop, Hive) and the implications of these infrastructures for the accurate and efficient analysis of big data for healthcare applications. Students will learn the mathematical, statistical, artificial intelligence, and modeling techniques that have been developed for analysis of big data, especially for healthcare applications. Also, it will describe the visualization techniques which are useful for displaying big data analysis results for meaningful interpretation of the results by humans. It will use current real world problems involving big data analytics in healthcare, including the Big Data to Knowledge (BD2K) initiative of the National Institutes of Health (NIH). Students will gain experience in applying the techniques of big data analytics to healthcare problems.

Learning Objectives:

Comprehend the infrastructures used in big data analysis and apply this comprehension to effectively use these infrastructures during data analysis